Impact of pre-biologic impairment on meeting domain-specific biologic responder definitions in patients with severe asthma
Bibliographic record
Abstract
There is little agreement on clinically useful criteria for identifying real-world responders to biologic treatments for asthma.To investigate the impact of pre-biologic impairment on meeting domain-specific biologic responder definitions in adults with severe asthma.This was a longitudinal, cohort study across 22 countries participating in the International Severe Asthma Registry (https://isaregistries.org/) between May 2017 and January 2023. Change in 4 asthma domains (exacerbation rate, asthma control, long-term oral corticosteroid [LTOCS] dose, and lung function) was assessed from biologic initiation to 1 year post-treatment (minimum 24 weeks). Pre- to post-biologic changes for responders and nonresponders were described along a categorical gradient for each domain derived from pre-biologic distributions (exacerbation rate: 0 to 6+/y; asthma control: well controlled to uncontrolled; LTOCS: 0 to >30 mg/d; percent-predicted forced expiratory volume in 1 second [ppFEV1]: <50% to ≥80%).Percentage of biologic responders (ie, those with a category improvement pre- to post-biologic) varied by domain and increased with greater pre-biologic impairment, increasing from 70.2% to 90.0% for exacerbation rate, 46.3% to 52.3% for asthma control, 31.1% to 58.5% for LTOCS daily dose, and 35.8% to 50.6% for ppFEV1. The proportion of patients having improvement post-biologic tended to be greater for anti-IL-5/5R compared with for anti-IgE for exacerbation, asthma control, and ppFEV1 domains, irrespective of pre-biologic impairment.Our results provide realistic outcome-specific post-biologic expectations for both physicians and patients, will be foundational to inform future work on a multidimensional approach to define and assess biologic responders and response, and may enhance appropriate patient selection for biologic therapies.The ISAR database has ethical approval from the Anonymous Data Ethics Protocols and Transparency (ADEPT) committee (ADEPT0218) and is registered with the European Union Electronic Register of Post-Authorization studies (ENCEPP/DSPP/23720). The study was designed, implemented, and reported in compliance with the European Network Centres for Pharmacoepidemiology and Pharmacovigilance (ENCEPP) Code of Conduct (EUPAS38288) and with all applicable local and international laws and regulation, and registered with ENCEPP (https://www.encepp.eu/encepp/viewResource.htm?id=38289). Governance was provided by ADEPT (registration number: ADEPT1220).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".